Conventional dehazing algorithms usually neglect the problem of uneven fog concentration, which is existed in the traffic monitoring image. To tackle this problem, a dehazing algorithm based on wavelength related physical imaging model is proposed. Firstly, a wavelength related physical imaging model is built for traffic monitoring imaging process, in terms of the correlation between wavelength and fog concentration. Secondly, according to the correlation of color and wavelength, a transmission estimation strategy based on the maximal fuzzy correlation segmentation is designed. Considering the fuzzy property of overlapping intensities and spatial correlation of scenes, maximal fuzzy correlation is used for obtaining partition information of different scenes which helps to design the data term of graph cut and enable the implementation of graph cut. Such an image segmentation strategy incorporates the thresholdbased segmentation into the spatialbased segmention, ensuring the spatial correlation, improving the precision and further avoiding misclassfication of white object. Finally, the atmospheric light can be predicted by the sky region in the segmentaion result, and haze can be removed. The experiments tested on the 500 synthetic images and realworld images demonstrate that the proposed algorithm can improve dehazing precision by 7% at least and shorten running time by 15% roughly, comparing with exising dehazing algorithms. Hence, the proposed method can be used for image dehazing in the traffic monitoring system.